Data Analytics Engineer (ID:3416)

• Bridge the gap between data engineering, data architecture, and data analysis by delivering clean, reliable, and analytics-ready data.
• Design, build, and maintain robust and scalable data pipelines to support repeatable and accessible data consumption.
• Transform raw data into structured, high-quality datasets suitable for analysis and reporting.
• Develop and maintain complex data models that represent business processes and entities.
• Implement flexible Data Vault models in Snowflake to support large-scale analytics and business intelligence.
• Write, optimize, and maintain complex SQL queries with a focus on performance, scalability, and data integrity.
• Monitor, troubleshoot, and proactively resolve issues in production data pipelines.
• Automate repetitive data processes using Python and scripting tools to improve efficiency and scalability.
• Collaborate closely with Data Engineers, Data Architects, Data Scientists, and Product Managers to deliver integrated data solutions.
• Contribute to the design and development of data products, enhancing existing components or creating new ones as needed.

• 6-8 years of experience in data analytics engineering, data engineering, or advanced analytics roles.
• Strong expertise in SQL and PL/SQL for data transformation and performance optimization.
• Hands-on experience with Snowflake and modern cloud data warehouses.
• Solid experience implementing Data Vault modelling techniques.
• Proficiency in Python for automation and data workflow orchestration.
• Experience with DBT (Data Build Tool) for data transformation and modelling.
• Strong understanding of data warehousing concepts and data modelling principles.
• Proven ability to work with complex and high-volume datasets.

You Should Possess the Ability to

• Translate business requirements into scalable, technical data solutions.
• Design and maintain analytics-ready datasets and reusable data models.
• Optimize data workflows for performance, reliability, and scalability.
• Automate data operations to improve efficiency and consistency.
• Influence design decisions aligned with architectural and engineering standards.
• Adapt to evolving technologies, tools, and analytics best practices.